GDRNPP for BOP2022

This repo provides code and models for GDRNPP.

Path setting

# recommend using soft links (ln -sf)
datasets/
├── BOP_DATASETS   # https://bop.felk.cvut.cz/datasets/
    ├──lm
    ├──lmo
    ├──ycbv
    ├──icbin
    ├──hb
    ├──itodd
    ├──tless
├── VOCdevkit
└── coco

Dependencies

See INSTALL.md

Detection

TODO

Pose Estimation

The difference between this repo and gdrn conference version mainly including:

• Domain Randomization: We used stronger domain randomization operations than the conference version during training.
• Network Architecture: We used a more powerful backbone Convnext rather than resnet-34, and two mask heads for predicting amodal mask and visible mask separately.
• Other training details, such as learning rate, weight decay, visible threshold, and bounding box type.

Training

./core/gdrn_modeling/train_gdrn.sh <config_path> <gpu_ids> (other args)

Testing

./core/gdrn_modeling/test_gdrn.sh <config_path> <gpu_ids> <ckpt_path> (other args)